2 edition of **Pseudo-linear systems** found in the catalog.

Pseudo-linear systems

Banks, Stephen P.

- 95 Want to read
- 26 Currently reading

Published
**1993**
by University of Sheffield, Dept. of Automatic Control and Systems Engineering in Sheffield
.

Written in English

**Edition Notes**

Series | Research report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.497, Research report (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.497. |

ID Numbers | |
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Open Library | OL13975462M |

Kulkarni: Modeling, Analysis, Design, and Control of Stochastic Systems Lehmann: Elements of Large-Sample Theory Lehmann: Testing Statistical Hypotheses, Second Edition Lehmann and Casella: Theory of Point Estimation, Second Edition Lindman: Analysis of Variance in Experimental Design Lindsey: Applying Generalized Linear Models. To enhance the system performance, the particle swarm optimization is adopted to optimize the RBF nerve center, an optimized RBF neural network inverse and a two-motor system is connected in series to form composite pseudo-linear system. This two-motor synchronous system can be decoupled into two independent linear subsystems for speed and tension.

12 hours ago In about pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. This paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the.

Book Description. With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. Emphasizing the time domain description, the author presents theorems to highlight the most important results, proofs to. CHAPTER LEAST SQUARES, PSEUDO-INVERSES, PCA Theorem Every linear system Ax = b,where A is an m× n-matrix, has a unique least-squares so-.

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Pseudo-linear algebra is the study of common properties of linear differential and difference operators. We introduce in this paper its basic objects (pseudo-derivations, skew polynomials, and pseudo-linear operators) and describe several recent algorithms on them, which, when applied in the differential and difference cases, yield algorithms for uncoupling and solving systems of linear Cited by: Pseudo-linear systems book A-1 Deriving Pseudo Linear Systems from Affine Dynamical Systems.

A-2 Linear State Structure Free Motions. A-3 Free Motions with an Affine Map. A-4 Free Motions with a Readout Maps.

A-5 Pseudo Linear Systems. A-6 Sophisticated Pseudo Linear Systems. The pseudo-linear (PL) form representation of non-linear dynamical systems has led to the concept of non-linear eigenvalues (NEValues) and non-linear eigenvectors (NEVectors). Abstract. Let the set Y of output’s values be a linear space over the field R.

In the reference [Matsuo and Hasegawa, ], pseudo linear systems were presented with a main theorem, which says that for any time-invariant input response map, there exist at least two canonical (quasi-reachable and cbservable) pseudo linear systems which realize, that is, faithfully describe it, and any two Author: Yasumichi Hasegawa.

This study focuses on the parameter identification problems of Pseudo-linear systems book systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique.

First, an auxiliary model-based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition-based RLS Cited by: Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique.

Author(s): Feng Ding 1, 2; Ling Xu 1; Fuad E. Alsaadi 3; Tasawar Hayat 3; DOI: /iet-cta; For access to this article, please select a purchase option. They introduce General Dynamical Systems, Linear Representation Systems, Affine Dynamical Systems, Pseudo Linear Systems, Almost Linear Systems and So-called Linear Systems for discrete-time and demonstrate the relationship between them and the other dynamical systems.

This book is intended for graduate students and researchers who study. This monograph extends Realization Theory to the discrete-time domain. It includes new results and constructs a new and very wide inclusion relation for various non-linear dynamical systems.

After establishing some features of discrete-time dynamical systems it presents results concerning systems which are proposed by the authors for the first time.

This paper considers the optimal control problem for the bilinear system based on state feedback. Based on the concept of relative order of the output with respect to the input, first we change a bilinear system to a pseudo linear system model through the coordinate transformation.

Then based on the theory of linear quadratic optimal control, the optimal controller is designed by solving the. This monograph deals with control problems of discrete-time dynamical systems which include linear and nonlinear input/output relations In its present second enlarged edition the control problems of linear and non-linear dynamical systems will be solved as algebraically as possible.

The author focuses on algebraic methods for the discussion of control problems of linear and non-linear dynamical systems. The book contains detailed examples to showcase the effectiveness of the presented method. The target audience comprises primarily research experts in the field of control theory, but the book may also be beneficial for.

the system is pseudo-linear. We don't see the cell's large nonlinear response to the DC potential because we only measure the cell current at the excitation frequency.

If the system is non-linear, the current response will contain harmonics of the excitation frequency. A harmonic is a frequency equal to an integer multipled. Example 1. Consider the pseudo-linear uncertain system, () where and.

By posing and by applying Theorem 5, the majorant system of the system () controlled with the control law. turns out to be () In Figure 3 the value of for is reported.

It is significant to note that for it is, i.e. unless 5%, in accord with Theorem 7. For it is. pseudo-linear systems. Otherwise, a large timing jitter would be observ ed at the receiv er end.

Acknowledgments: This work was partially supported by a sabbatical grant from the Portuguese. This book provides a comprehensive account of fiber-optic communication systems. The 3rd edition of this book is used worldwide as a textbook in many universities.

This 4th edition incorporates recent advances that have occurred, in particular two new chapters. One deals with the advanced modulation formats (such as DPSK, QPSK, and QAM) that are increasingly being used for improving spectral. ABSTRACTThis paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems.

A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems.

In order to further improve the parameter accuracy, a. The basic structure of the neural network inverse system is composed of the static neural network with several integrators and state feedback of the original system to approach the inverse system of the nonlinear systems.

Then it is connected to the original system in series to form a pseudo linear system. Book Author(s): Govind P. Agrawal. Search for more papers by this author. First Finally the chapter deals with pseudo‐linear systems in which fiber dispersion is used to broaden short optical pulses so much that their peak power is reduced by a large factor over most of the fiber link.

This is done so that the cell’s response is pseudo-linear. In a linear (or pseudo-linear) system, the current response to a sinusoidal potential will be a sinusoid at the same frequency but shifted in phase (see Figure 1).

Linearity is described in more detail in the following section. Figure 1. Sinusoidal Current Response in a Linear System. This book provides a comprehensive account of fiber-optic communication systems.

The 3rd edition of this book is used worldwide as a textbook in many universities. This 4th edition incorporates recent advances that have occurred, in particular two new chapters.

Pseudo-linear Lightwave Systems Control of Intrachannel Nonlinear Price: $. I am wondering the difference between them. Basically they do the same job at the end finding coefficients of parameters, but they look just different the way we find the coefficients. To me, Least.Get this from a library!

System theory of continuous time finite dimensional dynamical systems: the memories of Tsuyoshi Matsuo and R.E. Kalman. [Yasumichi Hasegawa] -- This book discusses the realization and control problems of finite-dimensional dynamical systems which contain linear and nonlinear systems. The author focuses on algebraic methods for the discussion.Introduction --Input/Output Maps --General Dynamical Systems --Linear Representation Systems --Affine Dynamical Systems --Pseudo Linear Systems --Almost Linear Systems --"So-called" Linear Systems.

Series Title: Lecture notes in control and information sciences. Other Titles: Realization Theory of Discrete-Time Dynamical Systems (Online.